st_408
Written by Aunoy Poddar July 21st, 2022
Process the puncta quantified raw data
current_file <- rstudioapi::getActiveDocumentContext()$path
output_file <- stringr::str_replace(current_file, '.Rmd', '.R')
knitr::purl(current_file, output = output_file)
file.edit(output_file)Import packages and functions
library(Seurat)Attaching SeuratObjectlibrary(tictoc)
library(ggplot2)
library(patchwork)
library(pheatmap)
library(RColorBrewer)
library(tidyverse)Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✔ tibble 3.1.6 ✔ dplyr 1.0.8
✔ tidyr 1.2.0 ✔ stringr 1.4.0
✔ readr 2.1.2 ✔ forcats 0.5.1
✔ purrr 0.3.4
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(gridExtra)
Attaching package: ‘gridExtra’
The following object is masked from ‘package:dplyr’:
combinelibrary(png)
library(cowplot)
Attaching package: ‘cowplot’
The following object is masked from ‘package:patchwork’:
align_plotslibrary(magick)Linking to ImageMagick 6.9.10.23
Enabled features: fontconfig, freetype, fftw, lcms, pango, webp, x11
Disabled features: cairo, ghostscript, heic, raw, rsvg
Using 80 threadslibrary(scales)
Attaching package: ‘scales’
The following object is masked from ‘package:purrr’:
discard
The following object is masked from ‘package:readr’:
col_factorLoad the data
data_dir = '/home/aunoy/st/arc_profiling/st_analysis/hand_annotated_data/rethresholded'
meta_dir = '/home/aunoy/st/arc_profiling/st_analysis/hand_annotated_data/overlay'
output_dir_plot = '/home/aunoy/st/arc_profiling/st_analysis/results/plots'
output_dir_tbls = '/home/aunoy/st/arc_profiling/st_analysis/results/tables'Merge both datasets and generate a metadata column that corresponds
to the cell
df_408 = data.frame()
for (file_name in list.files(data_dir)){
print(file_name)
if(grepl('164', file_name)){
next
}
#if(grepl('408_TC', file_name) | grepl('408_vMS', file_name)){
# next
#}
df_to_append <- read.table(file.path(data_dir, file_name), sep = ',', header = TRUE)
while(length(ind <- which(df_to_append$Image.Name == "")) > 0){
df_to_append$Image.Name[ind] <- df_to_append$Image.Name[ind -1]
}
colnames(df_to_append) <- toupper(colnames(df_to_append))
df_to_append <- df_to_append %>%
mutate(area = strsplit(file_name, '.csv')[[1]])
## Add relative_XY_position
if(!is_empty(df_408)){
df_to_append <- df_to_append %>%
dplyr::select(colnames(df_408))
}
df_408 <- rbind(df_408, df_to_append)
}[1] "164_CC.csv"
[1] "164_MS_CC.csv"
[1] "164_MS_TC.csv"
[1] "164_TC.csv"
[1] "408_CC.csv"
[1] "408_dMS_TC.csv"
[1] "408_MS_CC.csv"
[1] "408_TC.csv"
[1] "408_vMS_TC.csv"df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='_Cluster', replacement=''))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='[*]', replacement=''))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='X', replacement=''))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='L2_', replacement='L2-'))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='-L2', replacement='_L2'))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='Tc_12', replacement='TC_12'))
## Missing
df_408 = df_408[df_408$IMAGE.NAME != 'Layer1', ]
df_408 = df_408[df_408$IMAGE.NAME != 'TC_1', ]
df_408 = df_408[df_408$IMAGE.NAME != 'TC_18', ]
df_408 = df_408[df_408$IMAGE.NAME != 'TC_19', ]
#df_408$IMAGE.NAME = toupper(df_408$IMAGE.NAME)
unique(df_408$IMAGE.NAME) [1] "CC_Cortical1" "CC_Cortical2" "CC_L2-1" "CC_L2-2" "CC_L2-3" "TC_2" "TC_3" "TC_4"
[9] "TC_5" "TC_6" "TC_7" "TC_8" "TC_9" "TC_10" "CC_4" "CC_5"
[17] "CC_6" "CC_7" "CC_8" "CC_9" "CC_10" "CC_11" "CC_12" "TC_16"
[25] "TC_17" "TC_20" "TC_11" "TC_12" "TC_13" "TC_14" "TC_15" Now we know that everything is equal to one another, we should load the variable
images_ordered = c('TC_20', 'TC_17', 'TC_16', 'TC_15', 'TC_14', 'TC_13', 'TC_12', 'TC_11', 'TC_10', 'TC_9', 'TC_8', 'TC_7', 'TC_6', 'TC_5',
'TC_4', 'TC_3', 'TC_2', 'CC_4', 'CC_5', 'CC_6', 'CC_7', 'CC_8', 'CC_9', 'CC_10', 'CC_11', 'CC_12', 'CC_L2-3', 'CC_L2-2', 'CC_L2-1', 'CC_Cortical1', 'CC_Cortical2')x_horz = 1:length(images_ordered) * 35
y_horz = rep(0, length(images_ordered))
horz_embedding = data.frame()
df_408$X_horz = -1
df_408$Y_horz = -1
IMAGE_SIZE = 1024
## This is the size of an image in the global coordinate space
IMAGE_LEN = 25
images = list.files(meta_dir)
for(i in 1:length(images_ordered)){
image_name = images_ordered[i]
print(image_name)
split_names = strsplit(image_name, '_')
cortex = toupper(split_names[[1]][1])
number = split_names[[1]][2]
number_csv = paste0('_', number, '.csv')
filename = images[grepl(cortex, images) & grepl(number_csv, images) & grepl('408', images)]
coordinates = read.table(file.path(meta_dir, filename), sep = ',', header = TRUE)
## checked already that lists are equal, missing 1, 18, 19 for now, layer 1 and others
## so this is a little tricky, so need to get it right
## Remember, it is the top right that the coordinate is coming from, but
## the bottom right is the new coordinate space.
## so first when we get the original coordinate space, to set to relative
## of bottom would be the same X, but 1024 - Y
## push out the coordinates for better visualization
#x_repelled <- (512 - coordinates$X_Coordinate_In_pixels)
df_408[df_408$IMAGE.NAME == image_name, 'X_horz'] = (coordinates$X_Coordinate_In_pixels /
IMAGE_SIZE * IMAGE_LEN) + y_horz[i]
df_408[df_408$IMAGE.NAME == image_name, 'Y_horz'] = ((1024-coordinates$Y_Coordinate_In_pixels) /
IMAGE_SIZE * IMAGE_LEN) + x_horz[i]
}[1] "TC_20"
[1] "TC_17"
[1] "TC_16"
[1] "TC_15"
[1] "TC_14"
[1] "TC_13"
[1] "TC_12"
[1] "TC_11"
[1] "TC_10"
[1] "TC_9"
[1] "TC_8"
[1] "TC_7"
[1] "TC_6"
[1] "TC_5"
[1] "TC_4"
[1] "TC_3"
[1] "TC_2"
[1] "CC_4"
[1] "CC_5"
[1] "CC_6"
[1] "CC_7"
[1] "CC_8"
[1] "CC_9"
[1] "CC_10"
[1] "CC_11"
[1] "CC_12"
[1] "CC_L2-3"
[1] "CC_L2-2"
[1] "CC_L2-1"
[1] "CC_Cortical1"
[1] "CC_Cortical2"We have the coordinates for 408_TC and others
rownames(df_408) = 1:nrow(df_408)
jy_408 = df_408 %>%
dplyr::select(-c(area, IMAGE.NAME, X_horz, Y_horz)) %>%
t() %>%
CreateSeuratObject()just set everything from below 1 in ratio to zero
jy_408 <- NormalizeData(jy_408, scale.factor = 1e5) ###Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|normed = GetAssayData(jy_408, slot = 'data')
normed[normed < 3] = 0
jy_408 <- SetAssayData(jy_408, slot = 'data', normed)xycords = df_408 %>% dplyr::select(c('X', 'Y')) %>% as.matrix()Error in (function (cond) :
error in evaluating the argument 'x' in selecting a method for function 'as.matrix': Can't subset columns that don't exist.
✖ Column `X` doesn't exist.DimPlot(jy_408, #cells = grepl('CC', df_408$area),
cols = c('purple', 'grey'), reduction = "XY", pt.size = 0.2, group.by = 'gad1_true', order = which(jy_408$gad1_true)) + coord_fixed(ratio = 1)Error: Cannot find 'XY' in this Seurat objectjy_408 <- FindVariableFeatures(jy_408, selection.method = "vst")Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|all.genes <- rownames(jy_408)
jy_408 <- ScaleData(jy_408, features = all.genes)Centering and scaling data matrix
|
| | 0%
|
|====================================================================================================================| 100%jy_408 <- RunPCA(jy_408, approx = FALSE)Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
PC_ 1
Positive: VIP, ASCL1, CXCR4, RELN, SATB2, MAF1, KIA0319, EMX1, GAD1, CXCL12
PAX6, DLX2, SST, PROX1, LHX6, NCAM1
Negative: DCX, TBR1, LRP8, SCGN, COUPTF2, EGFR, CXCL14, TSHZ1, GSX2, EOMES
SP8, NKX2.1, CALB2, CXCR7, DCDC2, VLDLR
PC_ 2
Positive: TBR1, EOMES, KIA0319, DCDC2, LRP8, CALB2, DCX, SATB2, CXCL12, EGFR
EMX1, ASCL1, COUPTF2, PAX6, RELN, CXCR4
Negative: MAF1, TSHZ1, NKX2.1, SST, GAD1, DLX2, PROX1, SP8, GSX2, VIP
SCGN, LHX6, VLDLR, CXCL14, CXCR7, NCAM1
PC_ 3
Positive: COUPTF2, DLX2, PROX1, GAD1, CXCR4, LHX6, LRP8, TSHZ1, CXCL14, EOMES
NKX2.1, SP8, CALB2, CXCL12, DCDC2, VLDLR
Negative: SATB2, RELN, MAF1, PAX6, ASCL1, SCGN, SST, EMX1, GSX2, NCAM1
DCX, KIA0319, VIP, EGFR, CXCR7, TBR1
PC_ 4
Positive: NCAM1, TSHZ1, VLDLR, CXCL14, PROX1, EGFR, CXCR7, DCX, ASCL1, DCDC2
CALB2, PAX6, GSX2, SCGN, EOMES, KIA0319
Negative: LHX6, DLX2, LRP8, SP8, VIP, SST, COUPTF2, NKX2.1, TBR1, CXCR4
EMX1, RELN, CXCL12, MAF1, GAD1, SATB2
PC_ 5
Positive: GSX2, SP8, NKX2.1, SCGN, COUPTF2, CXCL14, EGFR, EMX1, TSHZ1, CALB2
CXCL12, TBR1, PAX6, SATB2, MAF1, KIA0319
Negative: SST, CXCR4, DCDC2, GAD1, PROX1, EOMES, DLX2, VIP, DCX, VLDLR
RELN, NCAM1, LHX6, ASCL1, CXCR7, LRP8 jy_408 <- FindNeighbors(jy_408, dims = 1:30)Computing nearest neighbor graph
Computing SNNjy_408 <- FindClusters(jy_408, resolution = 1.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5629
Number of communities: 12
Elapsed time: 0 secondsjy_408 <- RunUMAP(jy_408, dims = 1:30)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
22:58:53 UMAP embedding parameters a = 0.9922 b = 1.112
22:58:53 Read 1013 rows and found 30 numeric columns
22:58:53 Using Annoy for neighbor search, n_neighbors = 30
22:58:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:58:53 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f0471c3b151
22:58:53 Searching Annoy index using 1 thread, search_k = 3000
22:58:53 Annoy recall = 100%
22:58:54 Commencing smooth kNN distance calibration using 1 thread
22:58:54 Initializing from normalized Laplacian + noise
22:58:54 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:58:55 Optimization finishedDimPlot(jy_408, reduction = "umap", group.by = 'seurat_clusters') + NoAxes()
DimPlot(jy_408, reduction = "H", pt.size = 1, split.by = 'seurat_clusters') + NoAxes() + NoLegend()
Error: Cannot find 'H' in this Seurat objectjy_408.markers <- FindAllMarkers(jy_408, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
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group_by(cluster) %>%
slice_max(n = 32, order_by = avg_log2FC)jy_408_0v4.markers <- FindMarkers(jy_408, ident.1 = 10, ident.2 = 9, only.pos = TRUE)
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s # view results
head(jy_408_0v4.markers)breakpoints = 1:20/10+0.3
plots = list()
jy_408 <- FindNeighbors(jy_408, dims = 1:30)Computing nearest neighbor graph
Computing SNNi = 1
for (breakpoint in breakpoints){
jy_408 <- FindClusters(jy_408, resolution = breakpoint)
jy_408 <- RunUMAP(jy_408, dims = 1:30)
jy_408.markers <- FindAllMarkers(jy_408, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
labels = jy_408.markers %>%
group_by(cluster) %>%
slice_max(n = 1, order_by = avg_log2FC)
new.cluster.ids <- labels$gene
names(new.cluster.ids) <- levels(jy_408)
jy_408 <- RenameIdents(jy_408, new.cluster.ids)
plots[[i]] = DimPlot(jy_408, reduction = "umap", pt.size = 1, label = TRUE) + NoAxes() + NoLegend() + ggtitle(breakpoint)
i = i + 1
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7652
Number of communities: 4
Elapsed time: 0 seconds00:47:32 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:47:32 Read 1013 rows and found 30 numeric columns
00:47:32 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:47:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:47:32 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea23600a8b6
00:47:32 Searching Annoy index using 1 thread, search_k = 3000
00:47:32 Annoy recall = 100%
00:47:33 Commencing smooth kNN distance calibration using 1 thread
00:47:35 Initializing from normalized Laplacian + noise
00:47:35 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:47:37 Optimization finished
Calculating cluster 0
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|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7378
Number of communities: 4
Elapsed time: 0 seconds00:47:38 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:47:38 Read 1013 rows and found 30 numeric columns
00:47:38 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:47:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:47:38 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea236c2b11eb
00:47:38 Searching Annoy index using 1 thread, search_k = 3000
00:47:38 Annoy recall = 100%
00:47:39 Commencing smooth kNN distance calibration using 1 thread
00:47:41 Initializing from normalized Laplacian + noise
00:47:41 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:47:43 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++++++ | 17% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7112
Number of communities: 5
Elapsed time: 0 seconds00:47:44 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:47:44 Read 1013 rows and found 30 numeric columns
00:47:44 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:47:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:47:44 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea2372c56ce
00:47:44 Searching Annoy index using 1 thread, search_k = 3000
00:47:44 Annoy recall = 100%
00:47:45 Commencing smooth kNN distance calibration using 1 thread
00:47:47 Initializing from normalized Laplacian + noise
00:47:47 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:47:50 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++++++ | 17% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6893
Number of communities: 6
Elapsed time: 0 seconds00:47:50 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:47:50 Read 1013 rows and found 30 numeric columns
00:47:50 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:47:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:47:50 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea2323d2618c
00:47:50 Searching Annoy index using 1 thread, search_k = 3000
00:47:50 Annoy recall = 100%
00:47:51 Commencing smooth kNN distance calibration using 1 thread
00:47:54 Initializing from normalized Laplacian + noise
00:47:54 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:47:56 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++++++ | 17% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6698
Number of communities: 7
Elapsed time: 0 seconds00:47:56 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:47:56 Read 1013 rows and found 30 numeric columns
00:47:56 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:47:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:47:56 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea231eb9a310
00:47:56 Searching Annoy index using 1 thread, search_k = 3000
00:47:57 Annoy recall = 100%
00:47:58 Commencing smooth kNN distance calibration using 1 thread
00:48:00 Initializing from normalized Laplacian + noise
00:48:00 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:02 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++++++ | 17% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6526
Number of communities: 8
Elapsed time: 0 seconds00:48:02 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:02 Read 1013 rows and found 30 numeric columns
00:48:02 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:02 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea234f7fb3e
00:48:02 Searching Annoy index using 1 thread, search_k = 3000
00:48:03 Annoy recall = 100%
00:48:04 Commencing smooth kNN distance calibration using 1 thread
00:48:06 Initializing from normalized Laplacian + noise
00:48:06 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:08 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6360
Number of communities: 8
Elapsed time: 0 seconds00:48:09 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:09 Read 1013 rows and found 30 numeric columns
00:48:09 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:09 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea235f7b01b0
00:48:09 Searching Annoy index using 1 thread, search_k = 3000
00:48:09 Annoy recall = 100%
00:48:10 Commencing smooth kNN distance calibration using 1 thread
00:48:12 Initializing from normalized Laplacian + noise
00:48:12 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:14 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6194
Number of communities: 8
Elapsed time: 0 seconds00:48:15 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:15 Read 1013 rows and found 30 numeric columns
00:48:15 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:15 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea231ba68aa0
00:48:15 Searching Annoy index using 1 thread, search_k = 3000
00:48:15 Annoy recall = 100%
00:48:16 Commencing smooth kNN distance calibration using 1 thread
00:48:18 Initializing from normalized Laplacian + noise
00:48:18 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:21 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6027
Number of communities: 8
Elapsed time: 0 seconds00:48:21 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:21 Read 1013 rows and found 30 numeric columns
00:48:21 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:21 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea231f3146ec
00:48:21 Searching Annoy index using 1 thread, search_k = 3000
00:48:22 Annoy recall = 100%
00:48:23 Commencing smooth kNN distance calibration using 1 thread
00:48:25 Initializing from normalized Laplacian + noise
00:48:25 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:27 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5885
Number of communities: 9
Elapsed time: 0 seconds00:48:28 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:28 Read 1013 rows and found 30 numeric columns
00:48:28 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:28 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea233b648366
00:48:28 Searching Annoy index using 1 thread, search_k = 3000
00:48:28 Annoy recall = 100%
00:48:29 Commencing smooth kNN distance calibration using 1 thread
00:48:31 Initializing from normalized Laplacian + noise
00:48:31 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:33 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5744
Number of communities: 11
Elapsed time: 0 seconds00:48:34 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:34 Read 1013 rows and found 30 numeric columns
00:48:34 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:34 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea23ede78e9
00:48:34 Searching Annoy index using 1 thread, search_k = 3000
00:48:35 Annoy recall = 100%
00:48:36 Commencing smooth kNN distance calibration using 1 thread
00:48:38 Initializing from normalized Laplacian + noise
00:48:38 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:40 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5629
Number of communities: 12
Elapsed time: 0 seconds00:48:41 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:41 Read 1013 rows and found 30 numeric columns
00:48:41 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:41 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea231b39f59c
00:48:41 Searching Annoy index using 1 thread, search_k = 3000
00:48:41 Annoy recall = 100%
00:48:42 Commencing smooth kNN distance calibration using 1 thread
00:48:44 Initializing from normalized Laplacian + noise
00:48:44 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:46 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5519
Number of communities: 13
Elapsed time: 0 seconds00:48:47 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:47 Read 1013 rows and found 30 numeric columns
00:48:47 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:48 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea237dae3b8b
00:48:48 Searching Annoy index using 1 thread, search_k = 3000
00:48:48 Annoy recall = 100%
00:48:49 Commencing smooth kNN distance calibration using 1 thread
00:48:51 Initializing from normalized Laplacian + noise
00:48:51 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:53 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5412
Number of communities: 13
Elapsed time: 0 seconds00:48:54 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:54 Read 1013 rows and found 30 numeric columns
00:48:54 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:48:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:54 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea2352f0059c
00:48:54 Searching Annoy index using 1 thread, search_k = 3000
00:48:54 Annoy recall = 100%
00:48:56 Commencing smooth kNN distance calibration using 1 thread
00:48:58 Initializing from normalized Laplacian + noise
00:48:58 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:00 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5306
Number of communities: 13
Elapsed time: 0 seconds00:49:01 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:49:01 Read 1013 rows and found 30 numeric columns
00:49:01 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:49:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:01 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea23732ee5d6
00:49:01 Searching Annoy index using 1 thread, search_k = 3000
00:49:01 Annoy recall = 100%
00:49:02 Commencing smooth kNN distance calibration using 1 thread
00:49:04 Initializing from normalized Laplacian + noise
00:49:04 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:07 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5206
Number of communities: 14
Elapsed time: 0 seconds00:49:08 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:49:08 Read 1013 rows and found 30 numeric columns
00:49:08 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:49:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:08 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea2361a8b024
00:49:08 Searching Annoy index using 1 thread, search_k = 3000
00:49:08 Annoy recall = 100%
00:49:09 Commencing smooth kNN distance calibration using 1 thread
00:49:11 Initializing from normalized Laplacian + noise
00:49:11 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:13 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 13
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5113
Number of communities: 14
Elapsed time: 0 seconds00:49:14 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:49:14 Read 1013 rows and found 30 numeric columns
00:49:14 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:49:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:15 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea232bc4dda8
00:49:15 Searching Annoy index using 1 thread, search_k = 3000
00:49:15 Annoy recall = 100%
00:49:16 Commencing smooth kNN distance calibration using 1 thread
00:49:18 Initializing from normalized Laplacian + noise
00:49:18 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:20 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 13
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5026
Number of communities: 15
Elapsed time: 0 seconds00:49:21 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:49:21 Read 1013 rows and found 30 numeric columns
00:49:21 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:49:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:21 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea233e07d50e
00:49:21 Searching Annoy index using 1 thread, search_k = 3000
00:49:22 Annoy recall = 100%
00:49:23 Commencing smooth kNN distance calibration using 1 thread
00:49:25 Initializing from normalized Laplacian + noise
00:49:25 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:27 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++++++ | 17% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 13
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 14
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.4950
Number of communities: 15
Elapsed time: 0 seconds00:49:28 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:49:28 Read 1013 rows and found 30 numeric columns
00:49:28 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:49:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:28 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea236d2af9cb
00:49:28 Searching Annoy index using 1 thread, search_k = 3000
00:49:29 Annoy recall = 100%
00:49:30 Commencing smooth kNN distance calibration using 1 thread
00:49:32 Initializing from normalized Laplacian + noise
00:49:32 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:34 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 9 % ~00s
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Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.4873
Number of communities: 15
Elapsed time: 0 seconds00:49:35 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:49:35 Read 1013 rows and found 30 numeric columns
00:49:35 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:49:35 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:35 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea231c4182e5
00:49:35 Searching Annoy index using 1 thread, search_k = 3000
00:49:36 Annoy recall = 100%
00:49:37 Commencing smooth kNN distance calibration using 1 thread
00:49:39 Initializing from normalized Laplacian + noise
00:49:39 Commencing optimization for 500 epochs, with 38030 positive edges
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:41 Optimization finished
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s marrangeGrob(plots, nrow=2, ncol=2)
ml <- marrangeGrob(plots, nrow=2, ncol=2)
ggsave(filename = '408_sequential_clustering_umaps.pdf', path = file.path(output_dir_plot, '20220721_1'), ml)Saving 7.29 x 4.5 in image
plot_features_umap <- function(sobj, gene, pt.size = 3, space = "umap")
{
coordinates <- Embeddings(sobj, reduction = space)
expmat <- as.matrix(FetchData(sobj, gene))
gene_df <- as.data.frame(cbind(coordinates, expmat))
colnames(gene_df) <- c('X', 'Y', 'expr')
gene_df <- gene_df %>% dplyr::arrange(!is.na(expr), expr)
colors = c('grey90', 'grey90', '#CB2A55')
gene_df$expr[gene_df$expr == 0] = NA
plot <- ggplot(gene_df, aes(x = X, y = Y, color = expr)) + geom_point(size = pt.size, alpha = 0.8)+
theme_classic() + ggtitle(gene) + NoAxes() + NoLegend() + scale_color_gradient(na.value = colors[1], low = colors[2], high = colors[3], labels = NULL) + theme(title = element_text(face = 'bold', size = rel(1), hjust = 1))
return(plot)
}
plot_features_umap(jy_408, 'VIP')
genes = rownames(jy_408)
plots <- lapply(1:length(genes), function(i){
plot_features_umap(jy_408, genes[i], pt.size = 1)
})
umaps = plot_grid(plotlist = plots, label_size = 10)
ggsave(plot = umaps, filename = 'test_408_umapl_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 18, height = 8, dpi = 150)
plot_features_xy <- function(sobj, gene, pt.size = 12, space = "XY")
{
coordinates <- Embeddings(sobj, reduction = space)
expmat <- as.matrix(FetchData(sobj, gene))
gene_df <- as.data.frame(cbind(coordinates, expmat))
colnames(gene_df) <- c('X', 'Y', 'expr')
gene_df <- gene_df %>% dplyr::arrange(!is.na(expr), expr)
colors = c('grey90', 'grey90', '#CB2A55')
gene_df$expr[gene_df$expr == 0] = NA
plot <- ggplot(gene_df, aes(x = X, y = Y, color = expr)) + geom_point(size = pt.size, alpha = 0.8)+
theme_classic() + ggtitle(gene) + NoAxes() + NoLegend() + scale_color_gradient(na.value = colors[1], low = colors[2], high = colors[3], labels = NULL) + theme(title = element_text(face = 'bold', size = rel(1), hjust = 1))
return(plot)
}
plot_features_xy(jy_408[,df_408$IMAGE.NAME == 'TC_1'], 'GSX2')Error: No cells foundOrder the images
unique(df_408$IMAGE.NAME)
images_ordered = c('TC_Cortical3', 'TC_Cortical2', 'TC_Cortical1', 'TC_10', 'TC_9', 'TC_8', 'TC_7', 'TC_6', 'TC_5', 'TC_4', 'TC_3', 'TC_2','TC_1','CC_2','CC_3',
'CC_4', 'CC_5', 'CC_6', 'CC_7', 'CC_8', 'CC_9', 'CC_10',
'CC_L2-1', 'CC_L2-2', 'CC_L2-3', 'CC_Cortical1', 'CC_Cortical2')x_horz = 1:length(images_ordered) * 35
y_horz = rep(0, length(images_ordered))
horz_embedding = data.frame()
df_408$X_horz = -1
df_408$Y_horz = -1
images = list.files(meta_dir)
for(i in 1:length(images_ordered)){
image_name = images_ordered[i]
print(image_name)
split_names = strsplit(image_name, '_')
cortex = toupper(split_names[[1]][1])
number = split_names[[1]][2]
number_csv = paste0('_', number, '.csv')
filename = images[grepl(cortex, images) & grepl(number_csv, images) & grepl('164', images)]
coordinates = read.table(file.path(meta_dir, filename), sep = ',', header = TRUE)
if(image_name == "CC_L2-1"){
coordinates = coordinates[c(1:37, 39:nrow(coordinates)), ]
}
## checked already that lists are equal, missing 1, 18, 19 for now, layer 1 and others
## so this is a little tricky, so need to get it right
## Remember, it is the top right that the coordinate is coming from, but
## the bottom right is the new coordinate space.
## so first when we get the original coordinate space, to set to relative
## of bottom would be the same X, but 1024 - Y
## push out the coordinates for better visualization
#x_repelled <- (512 - coordinates$X_Coordinate_In_pixels)
df_408[df_408$IMAGE.NAME == image_name, 'X_horz'] = (coordinates$X_Coordinate_In_pixels /
IMAGE_SIZE * IMAGE_LEN) + y_horz[i]
df_408[df_408$IMAGE.NAME == image_name, 'Y_horz'] = ((1024-coordinates$Y_Coordinate_In_pixels) /
IMAGE_SIZE * IMAGE_LEN) + x_horz[i]
}hcoords = df_408 %>% dplyr::select(c('X_horz', 'Y_horz')) %>% as.matrix()
colnames(hcoords) <- c('pixel_1', 'pixel_2')
jy_408[["H"]] <- CreateDimReducObject(embeddings = hcoords, key = "pixel_", assay = DefaultAssay(jy_408))plot_features_vertical_spatial <- function(sobj, gene, pt.size = 0.5, space = "H", arc = TRUE)
{
coordinates <- Embeddings(sobj, reduction = space)
expmat <- as.matrix(FetchData(sobj, gene))
gene_df <- as.data.frame(cbind(coordinates, expmat))
colnames(gene_df) <- c('X', 'Y', 'expr')
gene_df <- gene_df %>% dplyr::arrange(!is.na(expr), expr)
colors = c('grey90', 'grey90', '#0f4c5c')
gene_df$expr[gene_df$expr == 0] = NA
plot <- ggplot(gene_df, aes(x = X, y = Y, color = expr)) + geom_point(size = pt.size, alpha = 1)+
theme_classic() + ggtitle(gene) + NoAxes() + NoLegend() +
coord_fixed(ratio = 0.5) + scale_color_gradient(na.value = colors[1], low = colors[2], high = colors[3], labels = NULL) + theme(title = element_text(face = 'bold', size = rel(0.5), hjust = 1))
if(arc){plot = plot + geom_hline(yintercept=660, linetype = "dashed",color = colors[3])}
return(plot)
}
plot_features_vertical_spatial(jy_408, gene = 'GSX2')
plot_features_vertical_spatial_smoothed <- function(sobj, gene, pt.size = 0.5, space = "H", arc = TRUE)
{
coordinates <- Embeddings(sobj, reduction = space)
expmat <- as.matrix(FetchData(sobj, gene))
gene_df <- as.data.frame(cbind(coordinates, expmat))
colnames(gene_df) <- c('X', 'Y', 'expr')
gene_df <- gene_df %>% dplyr::arrange(!is.na(expr), expr)
colors = c('grey90', 'grey90', '#0f4c5c')
gene_df$expr[gene_df$expr == 0] = NA
plot <- gene_df %>%
filter(!is.na(expr)) %>%
ggplot(aes(x = X, y = Y, color = expr)) +stat_density_2d(aes(fill = ..density..), geom = "raster", n = 400,contour = FALSE, interpolate = TRUE) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
#geom_bin2d(bins = 10) + #geom_point(size = pt.size, alpha = 1)+
theme_classic() + ggtitle(gene) + NoAxes() + NoLegend() +
coord_fixed(ratio = 0.5) + scale_color_gradient(na.value = colors[1], low = colors[2], high = colors[3], labels = NULL) + theme(title = element_text(face = 'bold', size = rel(0.5), hjust = 1))
if(arc){plot = plot + geom_hline(yintercept=660, linetype = "dashed",color = colors[3])}
return(plot)
}
plot_features_vertical_spatial_smoothed(jy_408, gene = 'PAX6')
genes = rownames(jy_408)
plots <- lapply(1:length(genes), function(i){
plot_features_vertical_spatial_smoothed(jy_408, genes[i], pt.size = 1)
})
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
verts
#ggsave(plot = verts, filename = 'test_408_vertical_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 18, height = 8, dpi = 150)Do the clustering stuff here
plot_clusters_vertical_spatial <- function(sobj, cluster, clustering = NULL, anterior = FALSE, cluster_color = '#CB2A55', pt.size = 1, space = "H", arc = TRUE)
{
cluster_identity = as.numeric(unlist(ifelse(is.null(clustering),
Idents(sobj),sobj[[clustering]]))) == (cluster)
coordinates <- Embeddings(sobj, reduction = space)
gene_df <- as.data.frame(cbind(coordinates, cluster_identity))
colnames(gene_df) <- c('X', 'Y', 'clust')
gene_df <- gene_df %>% dplyr::arrange(clust)
plot <- ggplot(gene_df, aes(x = X, y = Y, color = factor(clust))) + geom_point(size = pt.size, alpha = 1) +
theme_classic() + ggtitle(cluster) + NoAxes() + NoLegend() +
coord_fixed(ratio = 0.5) + theme(title = element_text(face = 'bold', size = rel(0.8), hjust = 1))
cluster_color = scales::hue_pal()(nrow(unique(sobj[[clustering]])))[cluster]
plot = plot + scale_colour_manual(values = c('grey90', cluster_color))
intercept = ifelse(anterior, 660, 484)
if(arc){plot = plot + geom_hline(yintercept=484, linetype = "dashed",color = cluster_color)}
return(plot)
}
plot_clusters_vertical_spatial(jy_408, pt.size = 1, cluster = 1, clustering = 'RNA_snn_res.1.5')
clusters = as.numeric(sort(unique(jy_408$RNA_snn_res.1.5)))
plots <- lapply(1:length(clusters), function(i){
plot_clusters_vertical_spatial(jy_408, cluster = clusters[i], pt.size = 1, clustering = 'RNA_snn_res.1.5')
})
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
ggsave(plot = verts, filename = 'test_408_vertical_cluster_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 6, height = 8, dpi = 150)plot_clusters_umap <- function(sobj, clustering, pt.size = 3, space = "umap")
{
coordinates <- Embeddings(sobj, reduction = space)
expmat <- sobj[[clustering]]
#expmat = as.character(Idents(sobj))
gene_df <- as.data.frame(cbind(coordinates, expmat))
colnames(gene_df) <- c('X', 'Y', 'expr')
gene_df$X = as.numeric(gene_df$X)
gene_df$Y = as.numeric(gene_df$Y)
summary_gene_df = gene_df %>% dplyr::group_by(expr) %>% dplyr::summarise(xmean = mean(X), ymean = mean(Y))
plot <- ggplot(gene_df, aes(x = X, y = Y, color = as.factor(expr))) + geom_point(size = pt.size, alpha = 0.8)+ geom_label(data = summary_gene_df,
mapping = aes(x = xmean,
y = ymean),
label = summary_gene_df$expr) +
theme_classic() + ggtitle(clustering) + NoAxes() + NoLegend() + theme(title = element_text(face = 'bold', size = rel(1), hjust = 1))
cluster_colors = scales::hue_pal()(nrow(unique(expmat)))
plot = plot + scale_colour_manual(values = cluster_colors)
return(plot)
}
p = plot_clusters_umap(jy_408, clustering ='RNA_snn_res.1.5')
p
scales::hue_pal()(3)DimPlot(jy_408, cells.highlight = list('migratory' = which(grepl('MS', df_408$area))), cols.highlight = '#CB2A55')small_dimplot <- function(sobj, grep_pattern){
dp = DimPlot(jy_408, cells.highlight = list(imp = which(grepl(grep_pattern, df_408$area))))
dp <- dp + scale_color_manual(values = c('grey90', '#0f4c5c'), labels=c('other', grep_pattern)) + NoAxes()
}
patterns = c('408_CC', 'MS_CC', 'vMS_TC', 'dMS_TC', '408_TC')
plots <- lapply(1:length(patterns), function(i){
small_dimplot(jy_408, grep_pattern = patterns[i])
})Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.areas = plot_grid(plotlist = plots, label_size = 10, nrow = 2)
ggsave(plot = areas, filename = 'test_408_area_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 12, height = 3, dpi = 150)DimPlot(jy_408, cells.highlight = list('CC' = which(grepl('CC', df_408$area))), cols.highlight = '#CB2A55')jy_164<- RenameCells(jy_164, c(outer('164_', 1:ncol(jy_164), FUN=paste0)))
jy_164$area = df_164$area
jy_408<- RenameCells(jy_408, c(outer('408_', 1:ncol(jy_408), FUN=paste0)))
jy_408$area = df_408$area
jy_all <- merge(jy_164, jy_408)jy_all <- NormalizeData(jy_all, scale.factor = 1e5) ###Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|normed = GetAssayData(jy_all, slot = 'data')
normed[normed < 3] = 0
jy_all <- SetAssayData(jy_all, slot = 'data', normed)jy_all <- FindVariableFeatures(jy_all, selection.method = "vst")Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|all.genes <- rownames(jy_all)
jy_all <- ScaleData(jy_all, features = all.genes)Centering and scaling data matrix
|
| | 0%
|
|=====================================================================================================================================================| 100%jy_all <- RunPCA(jy_all, approx = FALSE)Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
PC_ 1
Positive: ASCL1, CXCR4, KIA0319, SATB2, RELN, PAX6, CXCL12, EMX1, VIP, CALB2
DCDC2, DLX2, VLDLR, EOMES, SST, LHX6
Negative: DCX, SCGN, GSX2, NKX2.1, TSHZ1, TBR1, LRP8, COUPTF2, SP8, EGFR
CXCR7, CXCL14, PROX1, MAF1, GAD1, NCAM1
PC_ 2
Positive: GAD1, MAF1, VIP, LHX6, NKX2.1, PROX1, DLX2, CXCR4, SST, TSHZ1
SP8, GSX2, VLDLR, RELN, CXCR7, COUPTF2
Negative: TBR1, DCX, KIA0319, DCDC2, LRP8, EOMES, SATB2, EGFR, CALB2, CXCL12
NCAM1, EMX1, PAX6, ASCL1, CXCL14, SCGN
PC_ 3
Positive: NCAM1, RELN, ASCL1, PAX6, SATB2, SST, MAF1, EMX1, VLDLR, EGFR
CXCR7, CXCL14, TSHZ1, DCX, SCGN, GSX2
Negative: COUPTF2, LRP8, LHX6, EOMES, TBR1, SP8, GAD1, DLX2, CXCR4, PROX1
NKX2.1, CALB2, CXCL12, DCDC2, VIP, KIA0319
PC_ 4
Positive: SST, RELN, DLX2, LRP8, LHX6, PAX6, GAD1, CALB2, CXCL12, CXCR4
TBR1, VIP, ASCL1, SATB2, KIA0319, EMX1
Negative: TSHZ1, PROX1, CXCR7, NCAM1, SCGN, NKX2.1, EGFR, VLDLR, SP8, CXCL14
GSX2, DCX, DCDC2, COUPTF2, EOMES, MAF1
PC_ 5
Positive: SP8, SCGN, PAX6, DLX2, EMX1, NKX2.1, SATB2, COUPTF2, ASCL1, SST
RELN, PROX1, VLDLR, TBR1, DCX, KIA0319
Negative: GAD1, CXCL14, NCAM1, EGFR, VIP, LRP8, DCDC2, CXCR4, MAF1, CXCR7
GSX2, LHX6, CXCL12, EOMES, CALB2, TSHZ1 jy_all <- FindNeighbors(jy_all, dims = 1:30)Computing nearest neighbor graph
Computing SNNjy_all <- FindClusters(jy_all, resolution = 1.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6181
Number of communities: 13
Elapsed time: 0 secondsjy_all <- RunUMAP(jy_all, dims = 1:30)02:15:18 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:15:18 Read 1815 rows and found 30 numeric columns
02:15:18 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:15:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:15:18 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea2320b00f92
02:15:18 Searching Annoy index using 1 thread, search_k = 3000
02:15:18 Annoy recall = 100%
02:15:20 Commencing smooth kNN distance calibration using 1 thread
02:15:22 Initializing from normalized Laplacian + noise
02:15:22 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:15:25 Optimization finishedDimPlot(jy_all, reduction = "umap", group.by = 'seurat_clusters') + NoAxes()
breakpoints = 1:20/10 + 0.1
plots = list()
jy_all <- FindNeighbors(jy_all, dims = 1:30)Computing nearest neighbor graph
Computing SNNi = 1
for (breakpoint in breakpoints){
jy_all <- FindClusters(jy_all, resolution = breakpoint)
jy_all <- RunUMAP(jy_all, dims = 1:30)
jy_all.markers <- FindAllMarkers(jy_all, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
labels = jy_all.markers %>%
group_by(cluster) %>%
slice_max(n = 1, order_by = avg_log2FC)
new.cluster.ids <- labels$gene
names(new.cluster.ids) <- levels(jy_all)
jy_all <- RenameIdents(jy_all, new.cluster.ids)
plots[[i]] = DimPlot(jy_all, reduction = "umap", pt.size = 1, label = TRUE) + NoAxes() + NoLegend() + ggtitle(breakpoint)
i = i + 1
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8486
Number of communities: 3
Elapsed time: 0 seconds02:03:43 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:03:43 Read 1815 rows and found 30 numeric columns
02:03:43 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:03:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:03:43 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea237f2f9479
02:03:43 Searching Annoy index using 1 thread, search_k = 3000
02:03:44 Annoy recall = 100%
02:03:45 Commencing smooth kNN distance calibration using 1 thread
02:03:47 Initializing from normalized Laplacian + noise
02:03:47 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:03:50 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8136
Number of communities: 3
Elapsed time: 0 seconds02:03:50 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:03:50 Read 1815 rows and found 30 numeric columns
02:03:50 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:03:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:03:50 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea232ef73e4e
02:03:50 Searching Annoy index using 1 thread, search_k = 3000
02:03:51 Annoy recall = 100%
02:03:52 Commencing smooth kNN distance calibration using 1 thread
02:03:54 Initializing from normalized Laplacian + noise
02:03:54 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:03:57 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7872
Number of communities: 5
Elapsed time: 0 seconds02:03:58 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:03:58 Read 1815 rows and found 30 numeric columns
02:03:58 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:03:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:03:58 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea2325f2042
02:03:58 Searching Annoy index using 1 thread, search_k = 3000
02:03:58 Annoy recall = 100%
02:03:59 Commencing smooth kNN distance calibration using 1 thread
02:04:01 Initializing from normalized Laplacian + noise
02:04:01 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:04 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7669
Number of communities: 6
Elapsed time: 0 seconds02:04:05 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:05 Read 1815 rows and found 30 numeric columns
02:04:05 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:05 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea237fab9b2d
02:04:05 Searching Annoy index using 1 thread, search_k = 3000
02:04:06 Annoy recall = 100%
02:04:07 Commencing smooth kNN distance calibration using 1 thread
02:04:09 Initializing from normalized Laplacian + noise
02:04:09 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:12 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7476
Number of communities: 6
Elapsed time: 0 seconds02:04:12 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:12 Read 1815 rows and found 30 numeric columns
02:04:12 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:13 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea23d0515
02:04:13 Searching Annoy index using 1 thread, search_k = 3000
02:04:13 Annoy recall = 100%
02:04:14 Commencing smooth kNN distance calibration using 1 thread
02:04:16 Initializing from normalized Laplacian + noise
02:04:16 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:19 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7288
Number of communities: 7
Elapsed time: 0 seconds02:04:20 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:20 Read 1815 rows and found 30 numeric columns
02:04:20 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:20 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea2325fee09b
02:04:20 Searching Annoy index using 1 thread, search_k = 3000
02:04:21 Annoy recall = 100%
02:04:22 Commencing smooth kNN distance calibration using 1 thread
02:04:24 Initializing from normalized Laplacian + noise
02:04:24 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:27 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++++++ | 17% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7115
Number of communities: 8
Elapsed time: 0 seconds02:04:28 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:28 Read 1815 rows and found 30 numeric columns
02:04:28 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:28 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea2393f62a0
02:04:28 Searching Annoy index using 1 thread, search_k = 3000
02:04:28 Annoy recall = 100%
02:04:29 Commencing smooth kNN distance calibration using 1 thread
02:04:31 Initializing from normalized Laplacian + noise
02:04:32 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:34 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++++ | 17% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6943
Number of communities: 8
Elapsed time: 0 seconds02:04:35 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:35 Read 1815 rows and found 30 numeric columns
02:04:35 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:35 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea23559d44e3
02:04:35 Searching Annoy index using 1 thread, search_k = 3000
02:04:36 Annoy recall = 100%
02:04:37 Commencing smooth kNN distance calibration using 1 thread
02:04:39 Initializing from normalized Laplacian + noise
02:04:39 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:42 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++++ | 17% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6791
Number of communities: 9
Elapsed time: 0 seconds02:04:43 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:43 Read 1815 rows and found 30 numeric columns
02:04:43 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:43 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea23a90ae58
02:04:43 Searching Annoy index using 1 thread, search_k = 3000
02:04:44 Annoy recall = 100%
02:04:45 Commencing smooth kNN distance calibration using 1 thread
02:04:47 Initializing from normalized Laplacian + noise
02:04:47 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:50 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6667
Number of communities: 11
Elapsed time: 0 seconds02:04:51 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:51 Read 1815 rows and found 30 numeric columns
02:04:51 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:51 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea2320a7dbd6
02:04:51 Searching Annoy index using 1 thread, search_k = 3000
02:04:51 Annoy recall = 100%
02:04:52 Commencing smooth kNN distance calibration using 1 thread
02:04:55 Initializing from normalized Laplacian + noise
02:04:55 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:57 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6548
Number of communities: 11
Elapsed time: 0 seconds02:04:59 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:59 Read 1815 rows and found 30 numeric columns
02:04:59 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:04:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:59 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea23422f68e5
02:04:59 Searching Annoy index using 1 thread, search_k = 3000
02:04:59 Annoy recall = 100%
02:05:00 Commencing smooth kNN distance calibration using 1 thread
02:05:02 Initializing from normalized Laplacian + noise
02:05:02 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:05 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6429
Number of communities: 11
Elapsed time: 0 seconds02:05:07 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:07 Read 1815 rows and found 30 numeric columns
02:05:07 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:07 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea23467c885a
02:05:07 Searching Annoy index using 1 thread, search_k = 3000
02:05:07 Annoy recall = 100%
02:05:08 Commencing smooth kNN distance calibration using 1 thread
02:05:10 Initializing from normalized Laplacian + noise
02:05:10 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:13 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6301
Number of communities: 12
Elapsed time: 0 seconds02:05:15 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:15 Read 1815 rows and found 30 numeric columns
02:05:15 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:15 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea2368cdc611
02:05:15 Searching Annoy index using 1 thread, search_k = 3000
02:05:15 Annoy recall = 100%
02:05:16 Commencing smooth kNN distance calibration using 1 thread
02:05:18 Initializing from normalized Laplacian + noise
02:05:18 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:21 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 6 % ~00s
|+++++++ | 12% ~00s
|++++++++++ | 19% ~00s
|+++++++++++++ | 25% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6181
Number of communities: 13
Elapsed time: 0 seconds02:05:23 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:23 Read 1815 rows and found 30 numeric columns
02:05:23 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:23 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea231eb4d391
02:05:23 Searching Annoy index using 1 thread, search_k = 3000
02:05:23 Annoy recall = 100%
02:05:24 Commencing smooth kNN distance calibration using 1 thread
02:05:27 Initializing from normalized Laplacian + noise
02:05:27 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:29 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6079
Number of communities: 14
Elapsed time: 0 seconds02:05:31 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:31 Read 1815 rows and found 30 numeric columns
02:05:31 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:31 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea231da498b5
02:05:31 Searching Annoy index using 1 thread, search_k = 3000
02:05:31 Annoy recall = 100%
02:05:33 Commencing smooth kNN distance calibration using 1 thread
02:05:35 Initializing from normalized Laplacian + noise
02:05:35 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:38 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 13
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5984
Number of communities: 14
Elapsed time: 0 seconds02:05:39 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:39 Read 1815 rows and found 30 numeric columns
02:05:39 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:39 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea2329f22371
02:05:39 Searching Annoy index using 1 thread, search_k = 3000
02:05:40 Annoy recall = 100%
02:05:41 Commencing smooth kNN distance calibration using 1 thread
02:05:43 Initializing from normalized Laplacian + noise
02:05:43 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:46 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 13
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5887
Number of communities: 14
Elapsed time: 0 seconds02:05:47 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:47 Read 1815 rows and found 30 numeric columns
02:05:47 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:47 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea23e0da122
02:05:47 Searching Annoy index using 1 thread, search_k = 3000
02:05:48 Annoy recall = 100%
02:05:49 Commencing smooth kNN distance calibration using 1 thread
02:05:51 Initializing from normalized Laplacian + noise
02:05:51 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:54 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 13
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5792
Number of communities: 14
Elapsed time: 0 seconds02:05:56 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:56 Read 1815 rows and found 30 numeric columns
02:05:56 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:05:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:56 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea23481db9ed
02:05:56 Searching Annoy index using 1 thread, search_k = 3000
02:05:56 Annoy recall = 100%
02:05:57 Commencing smooth kNN distance calibration using 1 thread
02:05:59 Initializing from normalized Laplacian + noise
02:05:59 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:06:02 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 13
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5694
Number of communities: 14
Elapsed time: 0 seconds02:06:04 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:06:04 Read 1815 rows and found 30 numeric columns
02:06:04 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:06:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:06:04 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea237141cf31
02:06:04 Searching Annoy index using 1 thread, search_k = 3000
02:06:04 Annoy recall = 100%
02:06:06 Commencing smooth kNN distance calibration using 1 thread
02:06:08 Initializing from normalized Laplacian + noise
02:06:08 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:06:11 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 13
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5602
Number of communities: 15
Elapsed time: 0 seconds02:06:12 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:06:12 Read 1815 rows and found 30 numeric columns
02:06:12 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
02:06:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:06:12 Writing NN index file to temp file /tmp/RtmpUBNEaB/file4ea23591624a
02:06:12 Searching Annoy index using 1 thread, search_k = 3000
02:06:13 Annoy recall = 100%
02:06:14 Commencing smooth kNN distance calibration using 1 thread
02:06:16 Initializing from normalized Laplacian + noise
02:06:16 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:06:19 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 13
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 14
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s marrangeGrob(plots, nrow=2, ncol=2)
ml <- marrangeGrob(plots, nrow=2, ncol=2)
ggsave(filename = 'all_sequential_clustering_umaps.pdf', path = file.path(output_dir_plot, '20220721_1'), ml)Saving 7.29 x 4.5 in image
big_dimplot <- function(sobj, grep_pattern){
dp = DimPlot(sobj, cells.highlight = list(imp = which(grepl(grep_pattern, sobj$area))))
dp <- dp + scale_color_manual(values = c('grey90', '#1982c4'), labels=c('other', grep_pattern)) + NoAxes()
}
patterns = c('408_CC', '408_MS_CC', 'vMS_TC', 'dMS_TC', '408_TC', '164_CC', '164_MS_CC', '164_MS_TC', '164_TC')
plots <- lapply(1:length(patterns), function(i){
big_dimplot(jy_all, grep_pattern = patterns[i])
})Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.areas = plot_grid(plotlist = plots, label_size = 10, nrow = 3)
areas
ggsave(plot = areas, filename = 'test_all_area_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 14, height = 8, dpi = 150)plot_clusters_umap(jy_all, clustering ='RNA_snn_res.1.5', pt.size = 2.0)
jy_all.markers <- FindAllMarkers(jy_all, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
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| | 0 % ~calculating
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| | 0 % ~calculating
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| | 0 % ~calculating
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Warning message:
In fun(libname, pkgname) : couldn't connect to display ":0"jy_all.markers %>%
group_by(cluster) %>%
slice_max(n = 32, order_by = avg_log2FC)clusters_164 = jy_all$RNA_snn_res.1.5[1:ncol(jy_164)]
jy_164$unified_clusters = clusters_164
clusters_408 = jy_all$RNA_snn_res.1.5[(ncol(jy_164)+1):ncol(jy_all)]
jy_408$unified_clusters = clusters_408clusters = as.numeric(sort(unique(jy_408$unified_clusters)))
plots <- lapply(1:length(clusters), function(i){
plot_clusters_vertical_spatial(jy_408, cluster = clusters[i], pt.size = 1, clustering = 'unified_clusters', anterior = FALSE)
})
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
ggsave(plot = verts, filename = 'test_408_unified_vertical_cluster_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 5, height = 8, dpi = 150)clusters = as.numeric(sort(unique(jy_164$unified_clusters)))
plots <- lapply(1:length(clusters), function(i){
plot_clusters_vertical_spatial(jy_164, cluster = clusters[i], pt.size = 1, clustering = 'unified_clusters', anterior = TRUE)
})
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
ggsave(plot = verts, filename = 'test_164_unified_vertical_cluster_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 5, height = 8, dpi = 150)genes = rownames(markers2)
plots <- lapply(1:length(genes), function(i){
plot_features_umap(jy_all, genes[i], pt.size = 0.5)
})
umaps = plot_grid(plotlist = plots, label_size = 10, nrow = 1)
umaps
ggsave(plot = umaps, filename = 'cluster7_markers_all_umapl_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 8, height = 1, dpi = 150)
jy_all_7.markers <- FindMarkers(jy_all, ident.1 = 7, ident.2 = NULL, only.pos = FALSE)
| | 0 % ~calculating
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s markers2 = jy_all_7.markers %>%
slice_max(n = 5, order_by = avg_log2FC)
markers2new.cluster.ids = c('TBR1+/LRP8+ MS to TC',
'PROX1 and NKX2.1 Immature INs',
'Immature CALB2+ CGE interneuron',
'Excitatory CXCL12+ Neurons',
'Cajal Retzius Cells',
'Mature, SST+ MGE INs',
'Mature, VIP+ CGE INs',
'MAF1+/TSHZ1+ Immature INs',
'Layer 2 Excitatory Neurons',
'CXCR4+ Posterior, MS to TC INs',
'EMX1+/NKX2.1+ Anterior, MS to CC INs',
'EMX1+/LHX6+ Posterior, MS to CC INs',
'GSX2+/RELN+/PAX6+ Clump')
names(new.cluster.ids) <- levels(jy_all)
jy_all <- RenameIdents(jy_all, new.cluster.ids)library(ggrepel)Warning message:
In fun(libname, pkgname) : couldn't connect to display ":0"plot_clusters_umap <- function(sobj, clustering, pt.size = 3, space = "umap")
{
coordinates <- Embeddings(sobj, reduction = space)
#expmat <- sobj[[clustering]]
expmat = as.character(Idents(jy_all))
gene_df <- as.data.frame(cbind(coordinates, expmat))
colnames(gene_df) <- c('X', 'Y', 'expr')
gene_df$X = as.numeric(gene_df$X)
gene_df$Y = as.numeric(gene_df$Y)
summary_gene_df = gene_df %>% dplyr::group_by(expr) %>% dplyr::summarise(xmean = mean(X), ymean = mean(Y))
plot <- ggplot(gene_df, aes(x = X, y = Y, color = as.factor(expr))) + geom_point(size = pt.size, alpha = 0.8) + #+ geom_label_repel(data = summary_gene_df,
# mapping = aes(x = xmean,
#y = ymean),
#label = summary_gene_df$expr) +
theme_classic() + ggtitle(clustering) + NoAxes() + #NoLegend() +
theme(title = element_text(face = 'bold', size = rel(1), hjust = 1))
cluster_colors = scales::hue_pal()(length(unique(expmat)))
plot = plot + scale_colour_manual(values = cluster_colors)
return(plot)
}
plot_clusters_umap(jy_all, clustering = '', pt.size = 2.0)
genes = rownames(jy_408)
plots <- lapply(1:length(genes), function(i){
plot_features_vertical_spatial(jy_408, genes[i], pt.size = 1)
})
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
ggsave(plot = verts, filename = 'test_408_vertical_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 18, height = 8, dpi = 150)---
title: "st_408"
output: html_notebook
---

Written by Aunoy Poddar
July 21st, 2022

# Process the puncta quantified raw data
```{r eval=FALSE}
current_file <- rstudioapi::getActiveDocumentContext()$path
output_file <- stringr::str_replace(current_file, '.Rmd', '.R')
knitr::purl(current_file, output = output_file)
file.edit(output_file)
```

## Import packages and functions
```{r}
library(Seurat)
library(tictoc)
library(ggplot2)
library(patchwork)
library(pheatmap)
library(RColorBrewer)
library(tidyverse)
library(gridExtra)
library(png)
library(cowplot)
library(magick)
library(scales)
```

## Load the data
```{r}
data_dir = '/home/aunoy/st/arc_profiling/st_analysis/hand_annotated_data/rethresholded'
meta_dir = '/home/aunoy/st/arc_profiling/st_analysis/hand_annotated_data/overlay'
output_dir_plot = '/home/aunoy/st/arc_profiling/st_analysis/results/plots'
output_dir_tbls = '/home/aunoy/st/arc_profiling/st_analysis/results/tables'
```


### Merge both datasets and generate a metadata column that corresponds
### to the cell #
```{r}
df_408 = data.frame()
for (file_name in list.files(data_dir)){
  print(file_name)
  if(grepl('164', file_name)){
    next
  }
  #if(grepl('408_TC', file_name) | grepl('408_vMS', file_name)){
  #  next
  #}
  df_to_append <- read.table(file.path(data_dir, file_name), sep = ',', header = TRUE)
  while(length(ind <- which(df_to_append$Image.Name == "")) > 0){
    df_to_append$Image.Name[ind] <- df_to_append$Image.Name[ind -1]
  }
  
  colnames(df_to_append) <- toupper(colnames(df_to_append))
  df_to_append <- df_to_append %>%
    mutate(area = strsplit(file_name, '.csv')[[1]])
  
  ## Add relative_XY_position
  
  if(!is_empty(df_408)){
    df_to_append <- df_to_append %>%
          dplyr::select(colnames(df_408))
  }
  df_408 <- rbind(df_408, df_to_append)
}
```
```{r}
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='_Cluster', replacement=''))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='[*]', replacement=''))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='X', replacement=''))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='L2_', replacement='L2-'))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='-L2', replacement='_L2'))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='Tc_12', replacement='TC_12'))
## Missing
df_408 = df_408[df_408$IMAGE.NAME != 'Layer1', ]
df_408 = df_408[df_408$IMAGE.NAME != 'TC_1', ]
df_408 = df_408[df_408$IMAGE.NAME != 'TC_18', ]
df_408 = df_408[df_408$IMAGE.NAME != 'TC_19', ]
#df_408$IMAGE.NAME = toupper(df_408$IMAGE.NAME)
unique(df_408$IMAGE.NAME)
```
## Now we know that everything is equal to one another, we should load the variable

```{r}
images_ordered = c('TC_20', 'TC_17', 'TC_16', 'TC_15', 'TC_14', 'TC_13', 'TC_12', 'TC_11', 'TC_10', 'TC_9', 'TC_8', 'TC_7', 'TC_6', 'TC_5',
                   'TC_4', 'TC_3', 'TC_2', 'CC_4', 'CC_5', 'CC_6', 'CC_7', 'CC_8', 'CC_9', 'CC_10', 'CC_11', 'CC_12', 'CC_L2-3', 'CC_L2-2', 'CC_L2-1', 'CC_Cortical1', 'CC_Cortical2')
```

```{r}
x_horz = 1:length(images_ordered) * 35
y_horz = rep(0, length(images_ordered))
horz_embedding = data.frame()
df_408$X_horz = -1
df_408$Y_horz = -1
IMAGE_SIZE = 1024
## This is the size of an image in the global coordinate space
IMAGE_LEN = 25

images = list.files(meta_dir)
for(i in 1:length(images_ordered)){
    image_name = images_ordered[i]
    print(image_name)
    split_names = strsplit(image_name, '_')
    cortex = toupper(split_names[[1]][1])
    number = split_names[[1]][2]
    number_csv = paste0('_', number, '.csv')
    filename = images[grepl(cortex, images) & grepl(number_csv, images) & grepl('408', images)]
    coordinates = read.table(file.path(meta_dir, filename), sep = ',', header = TRUE)
    ## checked already that lists are equal, missing 1, 18, 19 for now, layer 1 and others
 
    ## so this is a little tricky, so need to get it right
    ## Remember, it is the top right that the coordinate is coming from, but
    ## the bottom right is the new coordinate space.
    ## so first when we get the original coordinate space, to set to relative
    ## of bottom would be the same X, but 1024 - Y
    
    ## push out the coordinates for better visualization
    #x_repelled <- (512 - coordinates$X_Coordinate_In_pixels)
    
    
    df_408[df_408$IMAGE.NAME == image_name, 'X_horz'] = (coordinates$X_Coordinate_In_pixels / 
                                                      IMAGE_SIZE * IMAGE_LEN) + y_horz[i]
    df_408[df_408$IMAGE.NAME == image_name, 'Y_horz'] = ((1024-coordinates$Y_Coordinate_In_pixels) / 
                                                      IMAGE_SIZE * IMAGE_LEN) + x_horz[i]
}
```

## We have the coordinates for 408_TC and others
```{r}
rownames(df_408) = 1:nrow(df_408)
jy_408 = df_408 %>%
  dplyr::select(-c(area, IMAGE.NAME, X_horz, Y_horz)) %>%
  t() %>%
  CreateSeuratObject()
```

## just set everything from below 1 in ratio to zero
```{r}
jy_408 <- NormalizeData(jy_408, scale.factor = 1e5) ###
normed = GetAssayData(jy_408, slot = 'data')
normed[normed < 3] = 0
jy_408 <- SetAssayData(jy_408, slot = 'data', normed)
```

```{r}
jy_408$gad1_true = normed['GAD1',] != 0 #& normed['SATB2',] == 0
```

```{r}
DimPlot(jy_408, #cells = grepl('CC', df_408$area), 
        cols = c('purple', 'grey'), reduction = "XY", pt.size = 0.2, group.by = 'gad1_true', order = which(jy_408$gad1_true)) + coord_fixed(ratio = 1)
```
```{r}
jy_408 <- FindVariableFeatures(jy_408, selection.method = "vst")
all.genes <- rownames(jy_408)
jy_408 <- ScaleData(jy_408, features = all.genes)
jy_408 <- RunPCA(jy_408, approx = FALSE)
jy_408 <- FindNeighbors(jy_408, dims = 1:30)
jy_408 <- FindClusters(jy_408, resolution = 1.5)
jy_408 <- RunUMAP(jy_408, dims = 1:30)

DimPlot(jy_408,  reduction = "umap", group.by = 'seurat_clusters') + NoAxes()
```

```{r}
DimPlot(jy_408, reduction = "H", pt.size = 1, split.by = 'seurat_clusters') + NoAxes() + NoLegend()
```
```{r}
jy_408.markers <- FindAllMarkers(jy_408, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
jy_408.markers %>%
   group_by(cluster) %>%
   slice_max(n = 32, order_by = avg_log2FC)
```
```{r}
jy_408_0v4.markers <- FindMarkers(jy_408, ident.1 = 10, ident.2 = 9, only.pos = TRUE)
# view results
head(jy_408_0v4.markers)
```


```{r}
breakpoints = 1:20/10+0.3
plots = list()
jy_408 <- FindNeighbors(jy_408, dims = 1:30)
i = 1
for (breakpoint in breakpoints){
  jy_408 <- FindClusters(jy_408, resolution = breakpoint)
  jy_408 <- RunUMAP(jy_408, dims = 1:30)
  jy_408.markers <- FindAllMarkers(jy_408, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
  labels = jy_408.markers %>%
   group_by(cluster) %>%
   slice_max(n = 1, order_by = avg_log2FC)
  new.cluster.ids <- labels$gene
  names(new.cluster.ids) <- levels(jy_408)
  jy_408 <- RenameIdents(jy_408, new.cluster.ids)
  plots[[i]] = DimPlot(jy_408, reduction = "umap", pt.size = 1, label = TRUE) + NoAxes() + NoLegend() + ggtitle(breakpoint)
  i = i + 1
}

marrangeGrob(plots, nrow=2, ncol=2)
ml <- marrangeGrob(plots, nrow=2, ncol=2)
ggsave(filename = '408_sequential_clustering_umaps.pdf', path = file.path(output_dir_plot, '20220721_1'), ml)
```

```{r}
plot_features_umap <- function(sobj, gene, pt.size = 3, space = "umap")
{
  coordinates <- Embeddings(sobj, reduction = space)
  expmat <- as.matrix(FetchData(sobj, gene))
  gene_df <- as.data.frame(cbind(coordinates, expmat))
  colnames(gene_df) <- c('X', 'Y', 'expr')
  gene_df <- gene_df %>% dplyr::arrange(!is.na(expr), expr)
  colors = c('grey90', 'grey90', '#CB2A55')
  gene_df$expr[gene_df$expr == 0] = NA
  plot <- ggplot(gene_df, aes(x = X, y = Y, color = expr)) + geom_point(size = pt.size, alpha = 0.8)+  
            theme_classic() + ggtitle(gene) + NoAxes() + NoLegend() +  scale_color_gradient(na.value = colors[1], low = colors[2], high = colors[3], labels = NULL)  + theme(title = element_text(face = 'bold', size = rel(1), hjust = 1)) 
  return(plot)
}
plot_features_umap(jy_408, 'VIP')
```
```{r fig.height=8, fig.width=18}
genes = rownames(jy_408)
plots <- lapply(1:length(genes), function(i){
    plot_features_umap(jy_408, genes[i], pt.size = 1)
  })
umaps = plot_grid(plotlist = plots, label_size = 10)
ggsave(plot = umaps, filename = 'test_408_umapl_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 18, height = 8, dpi = 150)

```
```{r}
plot_features_xy <- function(sobj, gene, pt.size = 12, space = "XY")
{
  coordinates <- Embeddings(sobj, reduction = space)
  expmat <- as.matrix(FetchData(sobj, gene))
  gene_df <- as.data.frame(cbind(coordinates, expmat))
  colnames(gene_df) <- c('X', 'Y', 'expr')
  gene_df <- gene_df %>% dplyr::arrange(!is.na(expr), expr)
  colors = c('grey90', 'grey90', '#CB2A55')
  gene_df$expr[gene_df$expr == 0] = NA
  plot <- ggplot(gene_df, aes(x = X, y = Y, color = expr)) + geom_point(size = pt.size, alpha = 0.8)+  
            theme_classic() + ggtitle(gene) + NoAxes() + NoLegend() +  scale_color_gradient(na.value = colors[1], low = colors[2], high = colors[3], labels = NULL)  + theme(title = element_text(face = 'bold', size = rel(1), hjust = 1)) 
  return(plot)
}
plot_features_xy(jy_408[,df_408$IMAGE.NAME == 'TC_1'], 'GSX2')
```
## Order the images
```{r}
unique(df_408$IMAGE.NAME)
images_ordered = c('TC_Cortical3', 'TC_Cortical2', 'TC_Cortical1', 'TC_10', 'TC_9', 'TC_8', 'TC_7', 'TC_6', 'TC_5', 'TC_4', 'TC_3', 'TC_2','TC_1','CC_2','CC_3',
           'CC_4', 'CC_5', 'CC_6', 'CC_7', 'CC_8', 'CC_9', 'CC_10',
           'CC_L2-1', 'CC_L2-2', 'CC_L2-3', 'CC_Cortical1', 'CC_Cortical2')
```

```{r}
x_horz = 1:length(images_ordered) * 35
y_horz = rep(0, length(images_ordered))
horz_embedding = data.frame()
df_408$X_horz = -1
df_408$Y_horz = -1

images = list.files(meta_dir)
for(i in 1:length(images_ordered)){
    image_name = images_ordered[i]
    print(image_name)
    split_names = strsplit(image_name, '_')
    cortex = toupper(split_names[[1]][1])
    number = split_names[[1]][2]
    number_csv = paste0('_', number, '.csv')
    filename = images[grepl(cortex, images) & grepl(number_csv, images) & grepl('164', images)]
    coordinates = read.table(file.path(meta_dir, filename), sep = ',', header = TRUE)
    if(image_name == "CC_L2-1"){
      coordinates = coordinates[c(1:37, 39:nrow(coordinates)), ]
    }
    ## checked already that lists are equal, missing 1, 18, 19 for now, layer 1 and others
 
    ## so this is a little tricky, so need to get it right
    ## Remember, it is the top right that the coordinate is coming from, but
    ## the bottom right is the new coordinate space.
    ## so first when we get the original coordinate space, to set to relative
    ## of bottom would be the same X, but 1024 - Y
    
    ## push out the coordinates for better visualization
    #x_repelled <- (512 - coordinates$X_Coordinate_In_pixels)
    
    
    df_408[df_408$IMAGE.NAME == image_name, 'X_horz'] = (coordinates$X_Coordinate_In_pixels / 
                                                      IMAGE_SIZE * IMAGE_LEN) + y_horz[i]
    df_408[df_408$IMAGE.NAME == image_name, 'Y_horz'] = ((1024-coordinates$Y_Coordinate_In_pixels) / 
                                                      IMAGE_SIZE * IMAGE_LEN) + x_horz[i]
}
```

```{r}
hcoords = df_408 %>% dplyr::select(c('X_horz', 'Y_horz')) %>% as.matrix()
colnames(hcoords) <- c('pixel_1', 'pixel_2')

jy_408[["H"]] <- CreateDimReducObject(embeddings = hcoords, key = "pixel_", assay = DefaultAssay(jy_408))
```

```{r}
plot_features_vertical_spatial <- function(sobj, gene, pt.size = 0.5, space = "H", arc = TRUE)
{
  coordinates <- Embeddings(sobj, reduction = space)
  expmat <- as.matrix(FetchData(sobj, gene))
  gene_df <- as.data.frame(cbind(coordinates, expmat))
  colnames(gene_df) <- c('X', 'Y', 'expr')
  gene_df <- gene_df %>% dplyr::arrange(!is.na(expr), expr)
  colors = c('grey90', 'grey90', '#0f4c5c')
  gene_df$expr[gene_df$expr == 0] = NA
  plot <- ggplot(gene_df, aes(x = X, y = Y, color = expr)) + geom_point(size = pt.size, alpha = 1)+  
            theme_classic() + ggtitle(gene) + NoAxes() + NoLegend() + 
    coord_fixed(ratio = 0.5) + scale_color_gradient(na.value = colors[1], low = colors[2], high = colors[3], labels = NULL)  + theme(title = element_text(face = 'bold', size = rel(0.5), hjust = 1)) 
  if(arc){plot = plot + geom_hline(yintercept=660, linetype = "dashed",color = colors[3])}
  return(plot)
}
plot_features_vertical_spatial(jy_408, gene = 'GSX2')
```

```{r}
plot_features_vertical_spatial_smoothed <- function(sobj, gene, pt.size = 0.5, space = "H", arc = TRUE)
{
  coordinates <- Embeddings(sobj, reduction = space)
  expmat <- as.matrix(FetchData(sobj, gene))
  gene_df <- as.data.frame(cbind(coordinates, expmat))
  colnames(gene_df) <- c('X', 'Y', 'expr')
  gene_df <- gene_df %>% dplyr::arrange(!is.na(expr), expr)
  colors = c('grey90', 'grey90', '#0f4c5c')
  gene_df$expr[gene_df$expr == 0] = NA
  plot <- gene_df %>%
    filter(!is.na(expr)) %>%
    ggplot(aes(x = X, y = Y, color = expr)) +stat_density_2d(aes(fill = ..density..), geom = "raster", n = 400,contour = FALSE, interpolate = TRUE) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
#geom_bin2d(bins = 10) + #geom_point(size = pt.size, alpha = 1)+  
            theme_classic() + ggtitle(gene) + NoAxes() + NoLegend() + 
    coord_fixed(ratio = 0.5) + scale_color_gradient(na.value = colors[1], low = colors[2], high = colors[3], labels = NULL)  + theme(title = element_text(face = 'bold', size = rel(0.5), hjust = 1)) 
  if(arc){plot = plot + geom_hline(yintercept=660, linetype = "dashed",color = colors[3])}
  return(plot)
}
plot_features_vertical_spatial_smoothed(jy_408, gene = 'PAX6')
```


```{r fig.height=8, fig.width=18}
genes = rownames(jy_408)
plots <- lapply(1:length(genes), function(i){
    plot_features_vertical_spatial_smoothed(jy_408, genes[i], pt.size = 1)
  })
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
verts
#ggsave(plot = verts, filename = 'test_408_vertical_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 18, height = 8, dpi = 150)
```

## Do the clustering stuff here 
```{r}
plot_clusters_vertical_spatial <- function(sobj, cluster, clustering = NULL, anterior = FALSE, cluster_color =  '#CB2A55', pt.size = 1, space = "H", arc = TRUE)
{
  cluster_identity = as.numeric(unlist(ifelse(is.null(clustering),
                                              Idents(sobj),sobj[[clustering]]))) == (cluster)
  coordinates <- Embeddings(sobj, reduction = space)
  gene_df <- as.data.frame(cbind(coordinates, cluster_identity))
  colnames(gene_df) <- c('X', 'Y', 'clust')
  gene_df <- gene_df %>% dplyr::arrange(clust)
  plot <- ggplot(gene_df, aes(x = X, y = Y, color = factor(clust))) + geom_point(size = pt.size, alpha = 1) +  
            theme_classic() + ggtitle(cluster) + NoAxes() + NoLegend() + 
    coord_fixed(ratio = 0.5)  + theme(title = element_text(face = 'bold', size = rel(0.8), hjust = 1)) 
  cluster_color = scales::hue_pal()(nrow(unique(sobj[[clustering]])))[cluster]
  plot = plot + scale_colour_manual(values = c('grey90', cluster_color))
  intercept = ifelse(anterior, 660, 484)
  if(arc){plot = plot + geom_hline(yintercept=484, linetype = "dashed",color = cluster_color)}
  return(plot)
}
plot_clusters_vertical_spatial(jy_408, pt.size = 1, cluster = 1, clustering = 'RNA_snn_res.1.5')
```


```{r}
clusters = as.numeric(sort(unique(jy_408$RNA_snn_res.1.5)))
plots <- lapply(1:length(clusters), function(i){
    plot_clusters_vertical_spatial(jy_408, cluster = clusters[i], pt.size = 1, clustering = 'RNA_snn_res.1.5')
  })
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
ggsave(plot = verts, filename = 'test_408_vertical_cluster_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 6, height = 8, dpi = 150)
```

```{r}
plot_clusters_umap <- function(sobj, clustering, pt.size = 3, space = "umap")
{
  coordinates <- Embeddings(sobj, reduction = space)
  #expmat <- sobj[[clustering]]
  expmat  = Idents(sobj)
  gene_df <- as.data.frame(cbind(coordinates, expmat))
  colnames(gene_df) <- c('X', 'Y', 'expr')
  gene_df$X = as.numeric(gene_df$X)
  gene_df$Y = as.numeric(gene_df$Y)
  summary_gene_df = gene_df %>% dplyr::group_by(expr) %>% dplyr::summarise(xmean = mean(X), ymean = mean(Y))
  plot <- ggplot(gene_df, aes(x = X, y = Y, color = as.factor(expr))) + geom_point(size = pt.size, alpha = 0.8)+ geom_label(data = summary_gene_df,
                                                                                                                 mapping = aes(x = xmean, 
                           y = ymean), 
                           label = summary_gene_df$expr) +
            theme_classic() + ggtitle(clustering) + NoAxes() + NoLegend()  + theme(title = element_text(face = 'bold', size = rel(1), hjust = 1)) 
  cluster_colors = scales::hue_pal()(nrow(unique(expmat)))
  plot = plot + scale_colour_manual(values = cluster_colors)
  return(plot)
}
p = plot_clusters_umap(jy_408, clustering ='RNA_snn_res.1.5')
p
```

```{r}
scales::hue_pal()(3)
```

```{r}
DimPlot(jy_408, cells.highlight = list('migratory' = which(grepl('MS', df_408$area))), cols.highlight = '#CB2A55')
```

```{r fig.height=3, fig.width=12}
small_dimplot <- function(sobj, grep_pattern){
  dp = DimPlot(jy_408, cells.highlight = list(imp = which(grepl(grep_pattern, df_408$area))))
  dp <- dp + scale_color_manual(values = c('grey90', '#1982c4'), labels=c('other', grep_pattern)) + NoAxes()
}

patterns = c('408_CC', 'MS_CC', 'vMS_TC', 'dMS_TC', '408_TC')
plots <- lapply(1:length(patterns), function(i){
    small_dimplot(jy_408, grep_pattern = patterns[i])
  })
areas = plot_grid(plotlist = plots, label_size = 10, nrow = 2)
ggsave(plot = areas, filename = 'test_408_area_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 12, height = 3, dpi = 150)
```
```{r}
DimPlot(jy_408, cells.highlight = list('CC' = which(grepl('CC', df_408$area))), cols.highlight = '#CB2A55')
```

```{r}
jy_164<- RenameCells(jy_164, c(outer('164_', 1:ncol(jy_164), FUN=paste0)))
jy_164$area = df_164$area
jy_408<- RenameCells(jy_408, c(outer('408_', 1:ncol(jy_408), FUN=paste0)))
jy_408$area = df_408$area
jy_all <- merge(jy_164, jy_408)
```
```{r}
jy_all <- NormalizeData(jy_all, scale.factor = 1e5) ###
normed = GetAssayData(jy_all, slot = 'data')
normed[normed < 3] = 0
jy_all <- SetAssayData(jy_all, slot = 'data', normed)
```

```{r}
jy_all <- FindVariableFeatures(jy_all, selection.method = "vst")
all.genes <- rownames(jy_all)
jy_all <- ScaleData(jy_all, features = all.genes)
jy_all <- RunPCA(jy_all, approx = FALSE)
jy_all <- FindNeighbors(jy_all, dims = 1:30)
jy_all <- FindClusters(jy_all, resolution = 1.5)
jy_all <- RunUMAP(jy_all, dims = 1:30)

DimPlot(jy_all,  reduction = "umap", group.by = 'seurat_clusters') + NoAxes()
```
```{r}
breakpoints = 1:20/10 + 0.1
plots = list()
jy_all <- FindNeighbors(jy_all, dims = 1:30)
i = 1
for (breakpoint in breakpoints){
  jy_all <- FindClusters(jy_all, resolution = breakpoint)
  jy_all <- RunUMAP(jy_all, dims = 1:30)
  jy_all.markers <- FindAllMarkers(jy_all, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
  labels = jy_all.markers %>%
   group_by(cluster) %>%
   slice_max(n = 1, order_by = avg_log2FC)
  new.cluster.ids <- labels$gene
  names(new.cluster.ids) <- levels(jy_all)
  jy_all <- RenameIdents(jy_all, new.cluster.ids)
  plots[[i]] = DimPlot(jy_all, reduction = "umap", pt.size = 1, label = TRUE) + NoAxes() + NoLegend() + ggtitle(breakpoint)
  i = i + 1
}

marrangeGrob(plots, nrow=2, ncol=2)
ml <- marrangeGrob(plots, nrow=2, ncol=2)
ggsave(filename = 'all_sequential_clustering_umaps.pdf', path = file.path(output_dir_plot, '20220721_1'), ml)
```
```{r fig.height=8, fig.width=14}
big_dimplot <- function(sobj, grep_pattern){
  dp = DimPlot(sobj, cells.highlight = list(imp = which(grepl(grep_pattern, sobj$area))))
  dp <- dp + scale_color_manual(values = c('grey90', '#1982c4'), labels=c('other', grep_pattern)) + NoAxes()
}
patterns = c('408_CC', '408_MS_CC', 'vMS_TC', 'dMS_TC', '408_TC', '164_CC', '164_MS_CC', '164_MS_TC',  '164_TC')
plots <- lapply(1:length(patterns), function(i){
    big_dimplot(jy_all, grep_pattern = patterns[i])
  })
areas = plot_grid(plotlist = plots, label_size = 10, nrow = 3)
areas
ggsave(plot = areas, filename = 'test_all_area_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 14, height = 8, dpi = 150)
```
```{r}

plot_clusters_umap(jy_all, clustering ='RNA_snn_res.1.5', pt.size = 2.0)
```

```{r}
jy_all.markers <- FindAllMarkers(jy_all, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
jy_all.markers %>%
   group_by(cluster) %>%
   slice_max(n = 32, order_by = avg_log2FC)
```


```{r}
clusters_164 = jy_all$RNA_snn_res.1.5[1:ncol(jy_164)]
jy_164$unified_clusters = clusters_164
clusters_408 = jy_all$RNA_snn_res.1.5[(ncol(jy_164)+1):ncol(jy_all)]
jy_408$unified_clusters = clusters_408
```

```{r fig.height=8, fig.width=4}
clusters = as.numeric(sort(unique(jy_408$unified_clusters)))
plots <- lapply(1:length(clusters), function(i){
    plot_clusters_vertical_spatial(jy_408, cluster = clusters[i], pt.size = 1, clustering = 'unified_clusters', anterior = FALSE)
  })
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
ggsave(plot = verts, filename = 'test_408_unified_vertical_cluster_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 5, height = 8, dpi = 150)
```
```{r fig.height=8, fig.width=5}
clusters = as.numeric(sort(unique(jy_164$unified_clusters)))
plots <- lapply(1:length(clusters), function(i){
    plot_clusters_vertical_spatial(jy_164, cluster = clusters[i], pt.size = 1, clustering = 'unified_clusters', anterior = TRUE)
  })
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
ggsave(plot = verts, filename = 'test_164_unified_vertical_cluster_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 5, height = 8, dpi = 150)
```

```{r fig.height=1, fig.width=8}
genes = rownames(markers2)
plots <- lapply(1:length(genes), function(i){
    plot_features_umap(jy_all, genes[i], pt.size = 0.5)
  })
umaps = plot_grid(plotlist = plots, label_size = 10, nrow = 1)
umaps
ggsave(plot = umaps, filename = 'cluster7_markers_all_umapl_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 8, height = 1, dpi = 150)

```

```{r}
jy_all_7.markers <- FindMarkers(jy_all, ident.1 = 7, ident.2 = NULL, only.pos = FALSE)
markers2 = jy_all_7.markers %>%
   slice_max(n = 5, order_by = avg_log2FC)
markers2
```
```{r}
new.cluster.ids = c('TBR1+/LRP8+ MS to TC', 
              'PROX1 and NKX2.1 Immature INs',
'Immature CALB2+ CGE interneuron',
 'Excitatory CXCL12+ Neurons',
'Cajal Retzius Cells',
'Mature, SST+ MGE INs',
 'Mature, VIP+ CGE INs',
 'MAF1+/TSHZ1+ Immature INs',
 'Layer 2 Excitatory Neurons',
 'CXCR4+ Posterior, MS to TC INs',
'EMX1+/NKX2.1+ Anterior, MS to CC INs',
 'EMX1+/LHX6+ Posterior, MS to CC INs',
 'GSX2+/RELN+/PAX6+ Clump')

```

```{r}
names(new.cluster.ids) <- levels(jy_all)
jy_all <- RenameIdents(jy_all, new.cluster.ids)
```

```{r}
library(ggrepel)
plot_clusters_umap <- function(sobj, clustering, pt.size = 3, space = "umap")
{
  coordinates <- Embeddings(sobj, reduction = space)
  #expmat <- sobj[[clustering]]
  expmat  = as.character(Idents(jy_all))
  gene_df <- as.data.frame(cbind(coordinates, expmat))
  colnames(gene_df) <- c('X', 'Y', 'expr')
  gene_df$X = as.numeric(gene_df$X)
  gene_df$Y = as.numeric(gene_df$Y)
  summary_gene_df = gene_df %>% dplyr::group_by(expr) %>% dplyr::summarise(xmean = mean(X), ymean = mean(Y))
  plot <- ggplot(gene_df, aes(x = X, y = Y, color = as.factor(expr))) + geom_point(size = pt.size, alpha = 0.8) + #+ geom_label_repel(data = summary_gene_df,
                                                                                                                # mapping = aes(x = xmean, 
                           #y = ymean), 
                           #label = summary_gene_df$expr) +
            theme_classic() + ggtitle(clustering) + NoAxes() + #NoLegend()  + 
    theme(title = element_text(face = 'bold', size = rel(1), hjust = 1)) 
  cluster_colors = scales::hue_pal()(length(unique(expmat)))
  plot = plot + scale_colour_manual(values = cluster_colors)
  return(plot)
}

plot_clusters_umap(jy_all, clustering = '', pt.size = 2.0)
```

```{r}
genes = rownames(jy_408)
plots <- lapply(1:length(genes), function(i){
    plot_features_vertical_spatial(jy_408, genes[i], pt.size = 1)
  })
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
ggsave(plot = verts, filename = 'test_408_vertical_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 18, height = 8, dpi = 150)
```

